中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 上海药物研究所
  3. 中国科学院上海药物研究所
  4. 新药研究国家重点实验室
SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction

文献类型：期刊论文

作者Cao, Duanhua7,8; Chen, Mingan5,6,7; Zhang, Runze1,7; Wang, Zhaokun1,7; Huang, Manlin4,7; Yu, Jie3,5,7; Jiang, Xinyu1,7; Fan, Zhehuan1,7; Zhang, Wei1,7; Zhou, Hao2
刊名NATURE METHODS
出版日期2024-11-27
页码24
ISSN号1548-7091
DOI10.1038/s41592-024-02516-y
英文摘要Accurately predicting protein-ligand interactions is crucial for understanding cellular processes. We introduce SurfDock, a deep-learning method that addresses this challenge by integrating protein sequence, three-dimensional structural graphs and surface-level features into an equivariant architecture. SurfDock employs a generative diffusion model on a non-Euclidean manifold, optimizing molecular translations, rotations and torsions to generate reliable binding poses. Our extensive evaluations across various benchmarks demonstrate SurfDock's superiority over existing methods in docking success rates and adherence to physical constraints. It also exhibits remarkable generalizability to unseen proteins and predicted apo structures, while achieving state-of-the-art performance in virtual screening tasks. In a real-world application, SurfDock identified seven novel hit molecules in a virtual screening project targeting aldehyde dehydrogenase 1B1, a key enzyme in cellular metabolism. This showcases SurfDock's ability to elucidate molecular mechanisms underlying cellular processes. These results highlight SurfDock's potential as a transformative tool in structural biology, offering enhanced accuracy, physical plausibility and practical applicability in understanding protein-ligand interactions. SurfDock is a method for predicting protein-ligand complex structures by leveraging multimodal protein information and generative diffusion frameworks. Its results can be generalized to unseen proteins and real-world settings.
WOS关键词DEEP LEARNING-MODEL ; SIDE-CHAIN ; DOCKING ; EFFICIENT ; BENCHMARKING ; LIBRARY
资助项目National Natural Science Foundation of China[T2225002] ; National Natural Science Foundation of China[82273855] ; National Natural Science Foundation of China[82204278] ; Strategic Priority Research Program of the Chinese Academy of sciences[XDB0850000] ; National Key Research and Development Program of China[2022YFC3400504] ; National Key Research and Development Program of China[2023YFC2305904] ; SIMM-SHUTCM Traditional Chinese Medicine Innovation Joint Research Program[E2G805H] ; Youth Innovation Promotion Association CAS[2023296] ; Shanghai Municipal Science and Technology Major Project ; Shanghai Advanced Research Institute, Chinese Academy of Science, China
WOS研究方向Biochemistry & Molecular Biology
WOS记录号WOS:001365170700001
出版者NATURE PORTFOLIO
源URL[http://119.78.100.183/handle/2S10ELR8/314774]  
专题新药研究国家重点实验室
通讯作者Zheng, Mingyue
作者单位1.Univ Chinese Acad Sci, Beijing, Peoples R China
2.Tsinghua Univ, Inst AI Ind Res AIR, Beijing, Peoples R China
3.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China
4.Nanchang Univ, Nanchang, Peoples R China
5.Lingang Lab, Shanghai, Peoples R China
6.ShanghaiTech Univ, Sch Phys Sci & Technol, Shanghai, Peoples R China
7.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai, Peoples R China
8.Zhejiang Univ, Innovat Inst Artificial Intelligence Med, Coll Pharmaceut Sci, Hangzhou, Zhejiang, Peoples R China
推荐引用方式
GB/T 7714		 Cao, Duanhua,Chen, Mingan,Zhang, Runze,et al. SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction[J]. NATURE METHODS,2024:24.
APA Cao, Duanhua.,Chen, Mingan.,Zhang, Runze.,Wang, Zhaokun.,Huang, Manlin.,...&Zheng, Mingyue.(2024).SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction.NATURE METHODS,24.
MLA Cao, Duanhua,et al."SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction".NATURE METHODS (2024):24.

入库方式： OAI收割

来源：上海药物研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。